mirror of
https://github.com/zebrajr/pytorch.git
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d4db1b90a1
Summary: Commit 479e4ce5 didn't end up solving the health checks firing and they are likely still caused by the remaining `assume` calls. Closes https://github.com/caffe2/caffe2/pull/1625 Differential Revision: D6573036 Pulled By: pietern fbshipit-source-id: eeb21bdd61dca0a632eb1ba9e529177ac2569bfd
301 lines
11 KiB
Python
301 lines
11 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import functools
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import hypothesis
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from hypothesis import given, settings, HealthCheck
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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class TestAdagrad(hu.HypothesisTestCase):
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@staticmethod
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def ref_adagrad(param_in, mom_in, grad, lr, epsilon, using_fp16=False):
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mom_in_f32 = mom_in
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param_in_f32 = param_in
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if(using_fp16):
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mom_in_f32 = mom_in.astype(np.float32)
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param_in_f32 = param_in.astype(np.float32)
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mom_out = mom_in_f32 + np.square(grad)
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grad_adj = lr * grad / (np.sqrt(mom_out) + epsilon)
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param_out = param_in_f32 + grad_adj
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if(using_fp16):
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return (param_out.astype(np.float16), mom_out.astype(np.float16))
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else:
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return (param_out.astype(np.float32), mom_out.astype(np.float32))
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@staticmethod
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def ref_row_wise_adagrad(param_in, mom_in, grad, lr, epsilon):
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mom_out = mom_in + np.mean(np.square(grad))
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grad_adj = lr * grad / (np.sqrt(mom_out) + epsilon)
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param_out = param_in + grad_adj
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return (param_out, mom_out)
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@given(inputs=hu.tensors(n=3),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**hu.gcs)
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def test_adagrad(self, inputs, lr, epsilon, gc, dc):
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param, momentum, grad = inputs
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lr = np.array([lr], dtype=np.float32)
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op = core.CreateOperator(
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"Adagrad",
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["param", "momentum", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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device_option=gc,
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)
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self.assertReferenceChecks(
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gc, op,
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[param, momentum, grad, lr],
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functools.partial(self.ref_adagrad, epsilon=epsilon))
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# Suppress filter_too_much health check.
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# Likely caused by `assume` call falling through too often.
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@settings(suppress_health_check=[HealthCheck.filter_too_much])
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@given(inputs=hu.tensors(n=3),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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data_strategy=st.data(),
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**hu.gcs)
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def test_sparse_adagrad(self, inputs, lr, epsilon,
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data_strategy, gc, dc):
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param, momentum, grad = inputs
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momentum = np.abs(momentum)
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lr = np.array([lr], dtype=np.float32)
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# Create an indexing array containing values that are lists of indices,
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# which index into grad
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indices = data_strategy.draw(
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hu.tensor(dtype=np.int64,
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elements=st.sampled_from(np.arange(grad.shape[0]))),
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)
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hypothesis.note('indices.shape: %s' % str(indices.shape))
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# For now, the indices must be unique
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hypothesis.assume(np.array_equal(np.unique(indices.flatten()),
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np.sort(indices.flatten())))
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# Sparsify grad
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grad = grad[indices]
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op = core.CreateOperator(
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"SparseAdagrad",
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["param", "momentum", "indices", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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device_option=gc)
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def ref_sparse(param, momentum, indices, grad, lr, ref_using_fp16=False):
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param_out = np.copy(param)
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momentum_out = np.copy(momentum)
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for i, index in enumerate(indices):
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param_out[index], momentum_out[index] = self.ref_adagrad(
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param[index],
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momentum[index],
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grad[i],
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lr,
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epsilon,
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using_fp16=ref_using_fp16
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)
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return (param_out, momentum_out)
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ref_using_fp16_values = [False]
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if dc == hu.gpu_do:
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ref_using_fp16_values.append(True)
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for ref_using_fp16 in ref_using_fp16_values:
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if(ref_using_fp16):
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print('test_sparse_adagrad with half precision embedding')
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momentum_i = momentum.astype(np.float16)
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param_i = param.astype(np.float16)
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else:
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print('test_sparse_adagrad with full precision embedding')
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momentum_i = momentum.astype(np.float32)
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param_i = param.astype(np.float32)
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self.assertReferenceChecks(
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gc, op, [param_i, momentum_i, indices, grad, lr, ref_using_fp16],
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ref_sparse
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)
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@given(inputs=hu.tensors(n=2),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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data_strategy=st.data(),
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**hu.gcs)
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def test_sparse_adagrad_empty(self, inputs, lr, epsilon,
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data_strategy, gc, dc):
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param, momentum = inputs
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momentum = np.abs(momentum)
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lr = np.array([lr], dtype=np.float32)
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grad = np.empty(shape=(0,) + param.shape[1:], dtype=np.float32)
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indices = np.empty(shape=(0,), dtype=np.int64)
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hypothesis.note('indices.shape: %s' % str(indices.shape))
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op = core.CreateOperator(
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"SparseAdagrad",
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["param", "momentum", "indices", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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device_option=gc)
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def ref_sparse(param, momentum, indices, grad, lr):
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param_out = np.copy(param)
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momentum_out = np.copy(momentum)
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return (param_out, momentum_out)
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ref_using_fp16_values = [False]
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if dc == hu.gpu_do:
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ref_using_fp16_values.append(True)
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for ref_using_fp16 in ref_using_fp16_values:
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if(ref_using_fp16):
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print('test_sparse_adagrad_empty with half precision embedding')
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momentum_i = momentum.astype(np.float16)
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param_i = param.astype(np.float16)
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else:
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print('test_sparse_adagrad_empty with full precision embedding')
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momentum_i = momentum.astype(np.float32)
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param_i = param.astype(np.float32)
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self.assertReferenceChecks(
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gc, op, [param_i, momentum_i, indices, grad, lr], ref_sparse
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)
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# Suppress filter_too_much health check.
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# Likely caused by `assume` call falling through too often.
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@settings(suppress_health_check=[HealthCheck.filter_too_much])
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@given(inputs=hu.tensors(n=2),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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data_strategy=st.data(),
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**hu.gcs)
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def test_row_wise_sparse_adagrad(self, inputs, lr, epsilon,
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data_strategy, gc, dc):
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param, grad = inputs
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lr = np.array([lr], dtype=np.float32)
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# Create a 1D row-wise average sum of squared gradients tensor.
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momentum = data_strategy.draw(
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hu.tensor1d(min_len=param.shape[0], max_len=param.shape[0],
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elements=hu.elements_of_type(dtype=np.float32))
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)
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momentum = np.abs(momentum)
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# Create an indexing array containing values which index into grad
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indices = data_strategy.draw(
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hu.tensor(dtype=np.int64,
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elements=st.sampled_from(np.arange(grad.shape[0]))),
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)
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# Note that unlike SparseAdagrad, RowWiseSparseAdagrad uses a moment
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# tensor that is strictly 1-dimensional and equal in length to the
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# first dimension of the parameters, so indices must also be
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# 1-dimensional.
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indices = indices.flatten()
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hypothesis.note('indices.shape: %s' % str(indices.shape))
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# The indices must be unique
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hypothesis.assume(np.array_equal(np.unique(indices), np.sort(indices)))
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# Sparsify grad
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grad = grad[indices]
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op = core.CreateOperator(
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"RowWiseSparseAdagrad",
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["param", "momentum", "indices", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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device_option=gc)
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def ref_row_wise_sparse(param, momentum, indices, grad, lr):
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param_out = np.copy(param)
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momentum_out = np.copy(momentum)
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for i, index in enumerate(indices):
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param_out[index], momentum_out[index] = self.ref_row_wise_adagrad(
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param[index], momentum[index], grad[i], lr, epsilon)
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return (param_out, momentum_out)
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self.assertReferenceChecks(
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gc, op,
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[param, momentum, indices, grad, lr],
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ref_row_wise_sparse)
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@given(inputs=hu.tensors(n=1),
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lr=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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data_strategy=st.data(),
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**hu.gcs)
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def test_row_wise_sparse_adagrad_empty(self, inputs, lr, epsilon,
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data_strategy, gc, dc):
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param = inputs[0]
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lr = np.array([lr], dtype=np.float32)
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momentum = data_strategy.draw(
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hu.tensor1d(min_len=param.shape[0], max_len=param.shape[0],
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elements=hu.elements_of_type(dtype=np.float32))
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)
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momentum = np.abs(momentum)
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grad = np.empty(shape=(0,) + param.shape[1:], dtype=np.float32)
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indices = np.empty(shape=(0,), dtype=np.int64)
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hypothesis.note('indices.shape: %s' % str(indices.shape))
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op = core.CreateOperator(
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"RowWiseSparseAdagrad",
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["param", "momentum", "indices", "grad", "lr"],
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["param", "momentum"],
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epsilon=epsilon,
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device_option=gc)
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def ref_row_wise_sparse(param, momentum, indices, grad, lr):
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param_out = np.copy(param)
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momentum_out = np.copy(momentum)
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return (param_out, momentum_out)
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self.assertReferenceChecks(
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gc, op,
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[param, momentum, indices, grad, lr],
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ref_row_wise_sparse)
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